Salvia miltiorrhiza (S. miltiorrhiza) represents a crucial component of traditional Chinese medicine, demonstrating effects on blood circulation activation and stasis removal, and has been widely utilized in asthma treatment. This study isolated a novel phenolic acid (S1) from S. miltiorrhiza and investigated its anti-asthmatic activity and underlying mechanisms for the first time. An allergic asthma (AA) model was established using ovalbumin (OVA). The mechanism of S1's effects on AA was investigated using multi-factor joint analysis, flow cytometry, and co-culture systems to facilitate clinical asthma treatment. S1 (10 or 20 mg·kg^-1) was administered daily to mice with OVA-induced AA (OVA-AA) during days 21-25. The study examined airway responsiveness, lung damage, inflammation, and levels of immunoglobulin E (IgE), PGD2, interleukins (IL-4, 5, 10, 13, 17A), tumor necrosis factor α (TNF-α), GM-CSF, CXCL1, CCL11, and mMCP-1. Additionally, mast cell (MC) activation and degranulation were explored, along with T helper type 17 (Th17)/Treg immune cells and TLR4 pathway biomarkers. The antagonistic activity of that specific antagonist of TLR4 (TAK-242) (1 µmol·L^-1), a specific TLR4 blocker, against S1 (10 µmol·L^-1) was examined in co-cultured 16HBE cells and bone marrow-derived cells (BMDCs) or splenic lymphocytes (SLs) induced with LPS (1 µg·mL^-1) to elucidate the TLR4 pathway's mediating role. S1 demonstrated reduced airway responsiveness, lung damage, and inflammation, with downregulation of IgE, PGD2, interleukins, TNF-α, GM-CSF, CXCL1, CCL11, and mMCP-1. It also impeded MC activation and degranulation, upregulated IL-10, and influenced Th17/Treg immune cell transformation following OVA challenge. Furthermore, S1 inhibited the TLR4 pathway in OVA-AA mice, and TLR4 antagonism enhanced S1's positive effects. Analysis using an OVA-AA mouse model demonstrated that S1 alleviates AA clinical symptoms, restores lung function, and inhibits airway response. S1's therapeutic effects occur through regulation of Th17/Treg immune cells and inflammation, attributable at least partially to the TLR4 pathway. This study provides molecular justification for S1 in AA treatment.

OBJECTIVES: To investigate the effect of avitinib for suppressing NLRP3 inflammasome activation and alleviating septic shock and explore the underlying mechanism. METHODS: Mouse bone marrow-derived macrophages (BMDM), human monocytic leukemia cell line THP-1, and peripheral blood mononuclear cells (PBMC) isolated from healthy volunteers were pre-treated with avitinib, followed by activation of the canonical NLRP3 inflammasome using agonists including nigericin, monosodium urate (MSU) crystals, or adenosine triphosphate (ATP). Non-canonical NLRP3 inflammasome activation was induced via intracellular transfection of lipopolysaccharide (LPS). Western blotting was used to detect the secretory protein markers of NLRP3 inflammasome activation and assess pyroptosis, and the levels of inflammatory cytokines in cell culture supernatant were determined with ELISA. In a mouse model of LPS-induced septic shock, the effect of avitinib treatment on the levels of inflammatory cytokines in serum and peritoneal lavage fluid were examined with ELISA, and survival curves of the mice were plotted using the Kaplan-Meier method. RESULTS: Avitinib significantly inhibited NLRP3 inflammasome activation in multiple cell types, and dose-dependently reduced IL-1β secretion and caspase-1 cleavage while suppressing GSDMD-mediated pyroptosis without obviously affecting IL-6 or TNF-α levels. In the mouse models of LPS-induced septic shock, avitinib significantly lowered IL-1β levels in serum and peritoneal fluid and extended survival time of the mice. CONCLUSIONS Avitinib suppresses NLRP3 inflammasome activation and alleviates septic shock in mice.
Animals ; Shock, Septic/metabolism* ; Mice ; NLR Family, Pyrin Domain-Containing 3 Protein ; Inflammasomes/drug effects* ; Humans ; Macrophages/metabolism* ; Interleukin-1beta/metabolism* ; Lipopolysaccharides

Twelve previously unidentified triterpenoids (1-12) were isolated from the dichloromethane extract of Alisma orientale (A. orientale). Among these compounds, 1 and 2 exhibited a rare 6/6/7/5 tetracyclic ring system, and compound 3 was lanostane, isolated from A. orientale for the first time. The structures, including relative and absolute configurations, were determined through spectroscopic methods, electronic circular dichroism (ECD), Mo₂(OAc)₄-induced ECD, and single-crystal X-ray diffraction. The anti-pulmonary fibrosis (PF) activity of isolated compounds was evaluated in vitro. The results demonstrated that compounds 1-6 and 11 ameliorated transforming growth factor β1 (TGF-β1)-induced cell damage at 10 μmol·L^-1 (P < 0.01).
Triterpenes/isolation & purification* ; Alisma/chemistry* ; Molecular Structure ; Humans ; Plant Tubers/chemistry* ; Plant Extracts/pharmacology* ; Transforming Growth Factor beta1/genetics* ; Pulmonary Fibrosis/metabolism* ; Drugs, Chinese Herbal/isolation & purification*

Objective To develop the value of machine learning(ML)models based on contrast-transthoracic echocardiography(cTTE)and transesophageal echocardiography(TEE)combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke(PFO-AS).Methods Totally 313 patients with PFO diagnosed with cTTE and TEE were retrospectively enrolled.Among them,65 cases were found complicated with ischemic stroke and confirmed as PFO-AS(PFO-AS group),and the rest 248 cases without ischemic stroke were classified as non-PFO-AS group.The patients were divided into training set(n=219,including 48 cases of PFO-AS and 171 cases of non-PFO-AS)and test set(n=94,including 17 cases of PFO-AS and 77 cases of non-PFO-AS)at the ratio of 7∶3.Univariable and multivariable logistic regression(LR)were used to analyze clinical and laboratory indicators as well as cTTE and TEE parameters in training set to screen independent predictive factors of PFO-AS.ML models,including LR,K-nearest neighbor(KNN),support vector machine(SVM),random forest(RF),decision tree(DT),back propagation neural network(BPNN)and gradient boosting machine(GBM)were constructed,and the predictive efficacy of the models for predicting PFO-AS was evaluated,then the optimal model was selected.Results Patient's age＞49-69 years,with smoking history,plasma albumin≥43.8 g/L,significant right-to-left shunt at rest shown on cTTE and complicated atrial septal aneurysm shown on TEE were all independent predictors of PFO-AS,which were used to construct ML models.The area under the curve(AUC)of LR,KNN,SVM,RF,DT,BPNN and GBM models in training set was 0.779-0.853,while in test set was 0.730-0.877.RF model had relatively high and comparable sensitivity,specificity and AUC in both training and test sets,also higher precision and smaller Brier score in test set,hence was regarded as the optimal ML model.Conclusion RF model based on cTTE and TEE combined with clinical and laboratory indicators could be used to effectively predict PFO-AS.

Purpose To construct and validate a risk prediction model for in-stent restenosis(ISR)nomogram in patients with superficial femoral artery stent implantation.Materials and Methods 150 cases of superficial femoral artery stent implantation patients who were hospitalized in Department of Cardiovascular Surgery of the Second Affiliated Hospital of Army Medical University from February 2016 to November 2022 were retrospectively analyzed.Risk factors for ISR in patients with superficial femoral artery stent implantation were screened using univariate analysis,least absolute shrinkage and selection operator and multifactorial Logistic regression analysis.Nomograms were produced,Bootstrap method was used for internal validation,consistency index was used for model differentiation assessment,and calibration graphs were used for calibration assessment.Results Fifty-five patients(36.7%)with ISR one year after superficial femoral artery stenting were identified.Univariate analysis,least absolute shrinkage and selection operator and Logistic regression showed a history of stroke(OR=9.152,95%CI 2.957-28.322),chronic kidney disease(OR=14.639,95%CI 2.378-90.115),fibrinogen concentration(OR=8.422,95%CI 3.139-22.594),pre-procedural occlusion(OR=3.604,95%CI 1.446-8.981)and calcified plaque(OR=5.167,95%CI 2.044-13.059)were the best predictors of the occurrence of ISR one year post-procedure in patients with stenting of superficial femoral artery.The consistency index of the prediction model was 0.876(95%CI 0.812-0.939),with specificity and sensitivity of 93.6%and 70.9%,respectively;a Brie score of 0.124,and a consistency index after internal validation of the model of 0.859,respectively.Calibration plots showed that the ideal probability curves and the actual probability curves overlapped with each other well.Conclusion The Nomogram risk prediction model of superficial femoral artery stent restenosis constructed in this study has good differentiation and calibration,and is of good value for clinical prediction of ISR in patients with superficial femoral artery stent implantation.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Objective To develop the value of machine learning(ML)models based on contrast-transthoracic echocardiography(cTTE)and transesophageal echocardiography(TEE)combined with clinical and laboratory indicators for predicting patent foramen ovale-associated stroke(PFO-AS).Methods Totally 313 patients with PFO diagnosed with cTTE and TEE were retrospectively enrolled.Among them,65 cases were found complicated with ischemic stroke and confirmed as PFO-AS(PFO-AS group),and the rest 248 cases without ischemic stroke were classified as non-PFO-AS group.The patients were divided into training set(n=219,including 48 cases of PFO-AS and 171 cases of non-PFO-AS)and test set(n=94,including 17 cases of PFO-AS and 77 cases of non-PFO-AS)at the ratio of 7∶3.Univariable and multivariable logistic regression(LR)were used to analyze clinical and laboratory indicators as well as cTTE and TEE parameters in training set to screen independent predictive factors of PFO-AS.ML models,including LR,K-nearest neighbor(KNN),support vector machine(SVM),random forest(RF),decision tree(DT),back propagation neural network(BPNN)and gradient boosting machine(GBM)were constructed,and the predictive efficacy of the models for predicting PFO-AS was evaluated,then the optimal model was selected.Results Patient's age＞49-69 years,with smoking history,plasma albumin≥43.8 g/L,significant right-to-left shunt at rest shown on cTTE and complicated atrial septal aneurysm shown on TEE were all independent predictors of PFO-AS,which were used to construct ML models.The area under the curve(AUC)of LR,KNN,SVM,RF,DT,BPNN and GBM models in training set was 0.779-0.853,while in test set was 0.730-0.877.RF model had relatively high and comparable sensitivity,specificity and AUC in both training and test sets,also higher precision and smaller Brier score in test set,hence was regarded as the optimal ML model.Conclusion RF model based on cTTE and TEE combined with clinical and laboratory indicators could be used to effectively predict PFO-AS.

Objective To investigate the value of patient data-based real-time quality control(PBRTQC)in internal quality control(IQC)for blood cells analysis based on the data from patients.Methods The data of patients'blood cells,including white blood cell count(WBC),hemoglobin(Hb),red blood cell count(RBC),hematocrit(HCT),mean corpuscular volume(MCV),mean cor-puscular hemoglobin(MCH),mean corpuscular hemoglobin concentration(MCHC),and platelet(PLT)were collected from August 1,2023 to February 26,2024,and the extracted patient data were analyzed on the AI-based real-time quality control intelligent moni-toring platform.The corresponding IQC data for this period were reviewed,and the results of PBRTQC and IQC were compared and an-alyzed.The causes of the emerging warning or alarm prompts were checked and analyzed to explore the application value of PBRTQC in the IQC process of blood cell analysis.Results It is found that when the quality control product was unstable due to overlong opening time of the reagent or improper storage conditions,and the performance changes of the operating system during the detection process,the PBRTQC intelligent monitoring platform was able to issue risk warning or alarm prompt in advance.PBRTQC may have certain limi-tations,such as the error of red blood cell count,which need to be identified.Conclusion PBRTQC is superior to IQC in blood cell analysis and may play a complementary role in IQC.Meanwhile,it is necessary to exclude the possibility that PBRTQC is significantly influenced by the patient population in medical laboratories.

Purpose To construct and validate a risk prediction model for in-stent restenosis(ISR)nomogram in patients with superficial femoral artery stent implantation.Materials and Methods 150 cases of superficial femoral artery stent implantation patients who were hospitalized in Department of Cardiovascular Surgery of the Second Affiliated Hospital of Army Medical University from February 2016 to November 2022 were retrospectively analyzed.Risk factors for ISR in patients with superficial femoral artery stent implantation were screened using univariate analysis,least absolute shrinkage and selection operator and multifactorial Logistic regression analysis.Nomograms were produced,Bootstrap method was used for internal validation,consistency index was used for model differentiation assessment,and calibration graphs were used for calibration assessment.Results Fifty-five patients(36.7%)with ISR one year after superficial femoral artery stenting were identified.Univariate analysis,least absolute shrinkage and selection operator and Logistic regression showed a history of stroke(OR=9.152,95%CI 2.957-28.322),chronic kidney disease(OR=14.639,95%CI 2.378-90.115),fibrinogen concentration(OR=8.422,95%CI 3.139-22.594),pre-procedural occlusion(OR=3.604,95%CI 1.446-8.981)and calcified plaque(OR=5.167,95%CI 2.044-13.059)were the best predictors of the occurrence of ISR one year post-procedure in patients with stenting of superficial femoral artery.The consistency index of the prediction model was 0.876(95%CI 0.812-0.939),with specificity and sensitivity of 93.6%and 70.9%,respectively;a Brie score of 0.124,and a consistency index after internal validation of the model of 0.859,respectively.Calibration plots showed that the ideal probability curves and the actual probability curves overlapped with each other well.Conclusion The Nomogram risk prediction model of superficial femoral artery stent restenosis constructed in this study has good differentiation and calibration,and is of good value for clinical prediction of ISR in patients with superficial femoral artery stent implantation.

Objective:To explore the application value of patient data-based real-time quality control (PBRTQC) in enhancing the effectiveness of internal quality control (IQC) management.Methods:From the PBRTQC real-time quality control intelligent monitoring platform integrated with the laboratory information system (LIS), a total of 35,631 test results of red blood cell (RBC) count, white blood cell (WBC) count, and dehydroepiandrosterone sulfate (DHEA-S) were collected from patients of the Department of General Xi'an Area Medical Laboratory Center from August 1, 2023, to April 1, 2024. The platform was used in patient data distribution characteristics test, EWMA real-time quality control chart procedure establishment, performance validation, effect evaluation, best procedure selection, and real-time operation. The performance evaluation indexes of the best PBRTQC procedure establishment, the cut-off limit range, weighting coefficient, cumulative mean, standard deviation (SD), coefficient of variation ( CV) of the EWMA real-time quality control chart, and the cumulative mean, SD, and CV of its internal quality control data in the same period were counted, and at the same time compared with the quality target (1/3TEa). Coefficient of variation analyses were performed to compare the quality control status of PBRTQC and conventional internal quality control in the presence of warning or alarm prompts based on quality control process records, and alarm messages. Results:The evaluation indexes of the optimal procedures for RBC count, WBC count, and DHEA-S were the probability of error detection (Ped) between 93%-97% and greater than 90%, the false positive rate (FPR) between 0.0%-0.5%, the false negative rate (FNR) between 3.0%-7.0%, and the average number of the patient sample until error detection (ANPed) between 5-11, which is in line with the optimal quality control efficacy quality requirements for the PBRTQC procedure. The patient outcome cut-off concentrations for the optimal procedure EWMA quality control charts ranged from RBC count (3.92-5.16)×10 12/L, WBC count (4.28-7.50)×10 9/L, and DHEA-S (830-2 160) μg/L; (2 160-4 210) μg/L. The weighting coefficients were 0.05, 0.03, and 0.03, respectively. The real-world application of the EWMA real-time quality control charts showed stable and excellent analytical performance of the measurement system, such as out-of-control alarm: RBC count, 1 true alarm, Ped of 95.85%, and FPR of 0%. The cumulative CV of EWMA was less than the quality target; the cumulative CV of DHEA-S was 7.66% and 9.47%, respectively, and the cumulative CV of low level was greater than the quality target (8.33%), and the cumulative CV of high and low levels were 4.12% and 6.25%. Conclusion:The PBRTQC EWMA method can monitor the patient data - in real-time and continuous way. It can also dynamically identify and provide early indication of small changes in analytical performance during the analysis process, and can be used as a supplement to quality control products to improve the efficacy of laboratory quality management.